Organized crime is a problem in retail stores. Organized retail crime (ORC) is characterized by gangs of criminals that work together to steal items from retail stores. ORC gangs include thieves (i.e., “boosters”) that shoplift or steal cargo for the gang. The thieves may move from store-to-store and may rob several stores in a day. Sometimes the thieves repeatedly rob the same store over time. ORC gangs may be wide ranging and may move from location-to-location, thereby improving the odds of avoiding capture.
After stealing the items, the thieves may convert the stolen items into value (e.g., money, drugs, etc.) in a variety of ways. Thieves may sell the stolen items to street-level fences who resell the items at discount stores, flea markets or through online auctions. The rise of social media provides easy access to potential buyers, so in some cases, thieves can sell items directly to customers as a result of an announcement (i.e., post) on social media. This social media aspect has eased the transaction component of the crime, expediting the completion of the crime-to-value process. For example, a thief may steal an item from a retail store and then post the item for sale immediately after leaving the store. If the thief immediately finds a buyer, the entire crime may transact within an hour. Thus, these crimes are increasingly attractive to criminals.
ORC thieves are typically repeat offenders and often commit their crimes according to some pattern. For example, thieves may target a store (or chain of stores) because it is an easy target due to lax security, a crowded environment, and/or an easy escape route (e.g., close to a highway). In addition, these thieves may operate during a particular time to aid their success (e.g., at a shift change, close to closing, etc.). Determining these patterns could help investigators catch these thieves and/or recover stolen property by preemptively detecting crimes and preparing accordingly.
Retail stores address crime investigation and asset protection/recovery in a variety of ways. One way utilizes a network of video cameras to monitor areas in or around a retail store location. The video captured by these systems include all activity—good and bad. As a result, investigating a crime may be difficult because of the quantity of video available to search. The severity of this problem compounds when investigators attempt to identify repeat offenders and/or correlate the surveillance data gathered at different stores to determine a repeat offender's pattern (i.e., modus operandi). For example, a single ORC investigation would require investigators to search hundreds of hours of video captured by multiple cameras at multiple store locations. Because a store may experience many crimes and because acting quickly is an important element in solving crimes and/or recovering stolen items, a traditional approach to investigation is impractical if not impossible. A need, therefore, exists for systems and methods to improve ORC investigations and preemptively detect likely crimes.
In particular, some ORC investigation improvements that are needed include (i) enabling users to easily share evidence collected at different times and locations, (ii) aggregating the shared evidence, (iii) projecting future criminal activity based on the aggregated evidence, and (iv) adjusting the search for a suspect or stolen item based on the projected future criminal activity.